Decision-Focused Wind Power Forecasting for Economic Dispatch Under Asymmetric Imbalance Costs

Abstract

Wind power forecasts are commonly trained to minimize statistical errors, although the thermal schedule based on a forecast ultimately incurs asymmetric recourse costs. To connect forecast training with this dispatch consequence, a dispatch-value-oriented forecasting (DVOF) framework is developed by coupling a Mamba-style forecaster with a solver-based differentiable economic-dispatch layer. The forecast determines the first-stage thermal schedule; after wind realization, load shedding and wind curtailment settle the imbalance, and the resulting recourse-cost gradient is propagated to the forecaster by KKT-based implicit differentiation. On Global Energy Forecasting Competition 2014 (GEFCom2014) data, DVOF reduces the day-ahead realized imbalance penalty from 55.5 to 26.6 cost units per hour relative to an MSE Loss model with the same backbone. The resulting forecast approaches the conservative operating point implied by the asymmetric recourse model. In the simplified single-area, single-period case, this result shows that the dispatch gradient can guide the forecaster toward a cost-relevant adjustment without prescribing or searching over a quantile level.